Simulated Jury as a Model for Teaching and Learning in a Remote Chemistry Teaching Methods Course
Bibliographic record
Abstract
In this article, we focus on a set of remote activities regarding simulated juries, judges’ evaluations, communication of verdicts, and a post-discussion conversation. These activities aimed to promote learning of the preservice teachers about the differences between remote and in-person teaching in a chemistry teaching methods course. The set of activities was developed at the beginning of the course, during the Pandemic Covid-19, and the interchanging of roles was applied by the teacher educator. These structured activities were accomplished through the preservice teachers’ interactions and their interactions with the teacher educator by means of arguments, explanations, dialogues, and injunctions. We used a multi-level method for discourse analysis to map, sample, and analyze virtual interactions, which afforded the following results: 1) the preservice teachers strongly engaged in the remote activities and constructed arguments and counterarguments in the simulated jury activities, showing active roles as knowledge producers in all set of activities; 2) several themes and subthemes were developed; 3) the teacher educator assumed the roles of instructor, manager of the discussions, and commentator; 4) the preservice teachers evaluated positively their experience with the set of activities; 5) the application of the multi-level method allowed us to make explicit the discursive moves of the participants and a model for the simulated juries and related activities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".